On the Tree Structure of Deep Convolutional Sum-Product Networks

Deep convolutional sum-product networks (DCSPNs) have very recently been introduced and shown to yield state-of-the-art results in image completion tasks. A DCSPN consists of a tree structure (a directed acyclic graph) coupled with parameters of the structure. Given that DCSPNs are in their infancy, many open questions remain regarding the properties and topology of its tree structure. In this paper, we undertake three investigations pertaining to the DCSPN structure. The first two studies revolve around the original structure put forth in the seminal paper. These studies increase the number of pooling layers and vary the hyperparameters in attempts to improve accuracy. The third inquiry suggests a new DCSPN tree structure that significantly lowers the training time at some modest expense of accuracy.